%0 Computer Program
%A Egberts, Ginger
%A Vermolen, Fred
%A Zuijlen, Paul van
%D 2023
%T Code supporting the paper: High-speed predictions of post-burn contraction using a neural network trained on 2D-finite element simulations
%U https://data.4tu.nl/articles/software/Code_supporting_the_paper_High-speed_predictions_of_post-burn_contraction_using_a_neural_network_trained_on_2D-finite_element_simulations/21257199/1
%R 10.4121/21257199.v1
%K Machine learning
%K Post-burn contraction
%K Feedforward neural network
%K Online application
%K Monte Carlo simulations
%X <p>This online resource shows three archived folders: Matlab, Python, and App that contain relevant code and data for the article: <em>High-speed predictions of post-burn contraction using a neural network trained on 2D-finite element simulations</em>. </p>
<p><br></p>
<p>Within the Matlab folder, one finds the codes used for the generation of the large dataset. Here, the file <em>Main.m</em> is the main file and from there, one can run the Monte Carlo simulation.</p>
<p><br></p>
<p>Within the Python folder, one finds the codes used for training the neural networks and creating the online application. The file Data.mat contains the data generated by the Matlab Monte Carlo simulation. The files <em>run_bound.py, run_rsa.py</em>, and <em>run_tse.py</em> train the neural networks, of which the best scoring ones are saved in the folder <em>Training</em>. The<em> DashApp</em> folder contains the code for the creation of the Application.</p>
<p><br></p>
<p>Within the App folder, one finds the executable <em>nn_R2_app.exe</em> that one can run, once the archived folder is unzipped. When running the app, it opens in a browser. This was checked in Windows.</p>
%I 4TU.ResearchData